TCP BBR (Bottleneck Bandwidth and Round-trip propagation time) is a new TCP variant developed at Google, and which, as of this year, is fully deployed in Googles internal WANs and used by services such as Google.com and YouTube. In contrast to other commonly used TCP variants, TCP BBR is not loss-based but model-based: It builds a model of the network path between communicating nodes in terms of bottleneck bandwidth and minimum round-trip delay and tries to operate at the point where all available bandwidth is used and the round-trip delay is at minimum. Although, TCP BBR has indeed resulted in lower latency and a more efficient usage of bandwidth in fixed networks, its performance over cellular networks is less clear. This paper studies TCP BBR in live mobile networks and through emulations, and compares its performance with TCP NewReno and TCP CUBIC, two of the most commonly used TCP variants. The results from these studies suggest that in most cases TCP BBR outperforms both TCP NewReno and TCP CUBIC, however, not so when the available bandwidth is scarce. In these cases, TCP BBR provides longer file completion times than any of the other two studied TCP variants. Moreover, competing TCP BBR flows do not share the available bandwidth in a fair way, something which, for example, shows up when shorter TCP BBR flows struggle to get its fair share from longer ones.

The sockets Applications Programming Interface (API) has become the standard way that applications access the transport services offered by the Internet Protocol stack. This paper presents NEAT, a user-space library that can provide an alternate transport API. NEAT allows applications to request the service they need using a new design that is agnostic to the specific choice of transport protocol underneath. This not only allows applications to take advantage of common protocol machinery, but also eases introduction of new network mechanisms and transport protocols. The paper describes the components of the NEAT library and illustrates the important benefits that can be gained from this new approach. NEAT is a software platform for developing advanced network applications that was designed in accordance with the standardization efforts on Transport Services (TAPS) in the Internet Engineering Task Force (IETF), but its features exceed the envisioned functionality of a TAPS system.

Due to the ever increasing data traffic demands, which are directly connected to increased energy consumption, it becomes challenging for operators to achieve capacity enhancement while limiting their electric bill. To that end, exploiting the context awareness of future cognitive networks is expected to play a key role. Next generation cellular networks are about to include a plethora of small cells, with users being able to communicate via multiple bands. Given that small cells are expected to be eventually as close as 50 m apart, not all of them will have a direct connection to the core network; thus, multihop communication through neighboring small cells may be required. In such architectures, the user association problem becomes challenging, with backhaul energy consumption being a definitive parameter. Thus, in this article, we study the user association problem in cognitive heterogeneous networks. We evaluate the existing approaches in terms of energy efficiency and show the potential of exploiting the available context-aware information (i.e., users' measurements and requirements, knowledge of the network architecture, and the available spectrum resources of each base station) to associate the users in an energy-efficient way, while maintaining high spectrum efficiency. Our study considers both the access network and backhaul energy consumption, while the performance of the association algorithms is evaluated under two different case study scenarios.